Presented at
Hackfest 2017,
Unknown date/time
(Unknown duration).
As we know the Android Application Industry from a security perspective, it is also quite well known that the Android platform is succeptible to malicious applications. And with the recent trend where all the vendors and customers going completely mobile, android has now become an attack surface for most of the malicious attacks. Moreover, the mechanisms used for android malware detection comprise of several known methods, and we also know that most of these mechanisms are permission based or based on API usage. But, when we go deeper in the analysis, we also realise the fact that these mechanisms are open to instruction level obfuscation techniques. Hence, we decided to bring in the approach of Machine Learning to the Android Malware analysis such as using the functional call graphs, and Hash Graph Kernel (Hido & Kashima) method which could be combined to implement a mechanism that could be used to find the similarities among the binaries while being stringent against these obfuscations used.
This Project implementation is based on well known machine learning algorithm which is Support Vector Machines for solving the problem of android malware analysis. This method involves the mechanism of detection of android malware by effeciently embedding the functional call graphs along the feature map. The gamechanger in this concept would be the optimal utilization of the SVM Algorithm(Support Vector Machine) that proves to be better than other approaches with a minimalistic amount of false positives found and a higher detection rate. With the help of clean & real malware android application samples, an explicit classification model is developed. The functional call graphs are extracted out of the android applications, then the linear-time graph kernel based explicit mapping is deployed in order to efficiently map all the call graphs to the explicit feature space. After the above methods are implemented, the SVM algorithm is then trained to thoroughly differentiate between the real and the malicious applications.
Presenters:
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Nikhil Kulkarni
Nikhil.P.K is an Independent Security Researcher and an International Trainer. His area of interest includes Web Application Penetration Testing, Network Forensics, Mobile Application Security. He is currently pursuing an extensive research in "Implementing Machine Learning into Security". He has presented his talks at International and National level Conferences and meets such as Cocon International Cyber Policing and Security Conference, DEFCON Bangalore 2012, Null Open Security Meet Bangalore, Null Open Security Meet Mysore. He is also a Bug Bounty Hunter and has been listed and Acknowledged in the Hall Of Fames of top Companies such as Microsoft, Apple, Adobe, Nokia, Engine Yard, AVIRA Antivirus, etc. He will also be presenting this paper at "Nuit Du Hack" conference in Paris on 24th June 2017.
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